The rapid proliferation of diverse programming languages presents both opportunities and challenges for developing multilingual code LLMs. While existing techniques often train code LLMs by simply aggregating multilingual code data, few explore the deeper relationships between programming languages and how such relationships can be utilized to optimize both training and inference. In this work, we investigate two fundamental questions: (1) What are the deep linguistic relationships among programming languages? and (2) How can these relationships be leveraged to improve multilingual code LLMs? We propose an embedding-based framework to uncover the latent families of programming languages. Our approach begins by defining 21 primary linguistic features of programming languages, such as variable definition, control structures, and method declarations, and then employs LLMs to generate feature-aligned code samples across multiple languages. By embedding these semantically parallel code snippets from 19 languages, we construct a similarity matrix and perform hierarchical clustering to uncover inherent language relationships. Our analysis reveals clear hierarchical structures among programming languages. Closely related languages form well-defined clusters (e.g., C, C++, Java, and Swift group together), while Go exhibits as a “lingua franca” with the highest cross-language similarity. Building on the uncovered language families, we propose three strategies to enhance multilingual LLM training: transfer learning across linguistically related languages, linguistic proximity-guided curriculum learning, and centroid-based intermediary code translation. Experiments on four code intelligence tasks demonstrate that our methods significantly improve multilingual LLM performance. This work offers a universal perspective on programming languages and advances more effective strategies for multilingual code LLM training.
Yun et al. (2026) studied this question.